Search Results (1,382)

Urban markets are key nodes for the persistence and adaptation of traditional edible plant knowledge, linking rural production with urban consumption. This study was based on monthly market surveys conducted throughout 2025 in an urban market in Baise City, Guangxi, China. A total of 54 edible plant taxa were recorded, including both native and introduced species, with herbaceous plants predominating alongside climbers, trees, and grasses. Ethnobotanical data were obtained through semi-structured interviews with 40 local informants (20 men and 20 women, aged 25–65 years) selected using purposive sampling, focusing on individuals actively involved in purchasing and preparing edible plants. High Cultural Food Significance Index (CFSI) values highlighted culturally central taxa, including Allium ascalonicum L., × Brassarda juncea (L.) Su Liu & Z.H. Feng, and Houttuynia cordata Thunb., reflecting frequent use and culinary–medicinal integration. Fidelity Level (FL) analyses identified species with strong consensus for specific therapeutic applications, such as × B. juncea, Alpinia galanga (L.) Willd., and Nelumbo nucifera Gaertn., while Informant Consensus Factor (FIC) values indicated moderate to high agreement across gastrointestinal, respiratory, inflammatory, and other health categories. These results underscore the persistence of the “food as medicine” concept, showing that edible plants function simultaneously as nutritional and preventive healthcare resources. The overlap of culinary and medicinal roles demonstrates dynamic food–medicine integration, with urban markets acting as cultural hubs that maintain dietary diversity, household food security, and ethnobotanical knowledge. Future studies should incorporate ethnozoological resources and longitudinal monitoring to capture the full scope of urban food–medicine systems. Full article

Accurate lung tumor segmentation using computed tomography (CT) scans is needed for efficient tumor treatment. However, the development of deep learning models is often constrained by strict patient privacy regulations that limit direct data sharing. This work presents a system that enables multi-institutional collaboration while training high-quality lung tumor segmentation models without requiring access to sensitive patient data. The proposed framework features the AuraViT suite, which includes the standard AuraViT—a hybrid model with 136 million parameters that combines a Vision Transformer (ViT) encoder, Atrous Spatial Pyramid Pooling (ASPP), and attention-gated residual connections—and the Lightweight AuraViT (LAURA) family (Small, Tiny, and Mobile). These variants are designed for resource-constrained environments and potential edge deployment scenarios. Training is conducted on publicly available datasets (MSD Lung and NSCLC) in a simulated five-client federated learning setup that emulates collaboration among institutions while ensuring patient privacy. The framework uses a federated learning setup with FedProx, adaptive weighted aggregation, and a dynamic virtual client strategy to handle data and system differences. The framework is further evaluated through ablation studies on model architecture and feature importance. The results show that the standard AuraViT-FL achieves a global mean Dice score of 80.81%, while maintaining performance close to centralized training. Additionally, the LAURA variations show a better trade-off between accuracy and efficiency. Notably, the Mobile variant with ∼5 M parameters reduces model complexity by over 96% while maintaining competitive performance (82.96% Dice on MSD Lung). Full article

Background: CD37, an antigen highly expressed in B-cell malignancies, served as the target in the LYMRIT-37-01 Part B (PARADIGME) and Part C studies employing a single intravenous injection of the radioimmunoconjugate ¹⁷⁷Lu–lilotomab satetraxetan (Betalutin^®). Methods: Patients with follicular lymphoma (FL), grades I–IIIa, who had received at least two previous lines of therapy and were refractory to at least one previous regimen with rituximab or an anti-CD20 agent, were included. They were randomized to receive either a 40 mg lilotomab pretreatment and an activity dosage of 15 MBq/kg Betalutin (“40/15” regimen) or 100 mg/m² of lilotomab and 20 MBq/kg of Betalutin (“100/20” regimen). In total, 109 patients were enrolled and received Betalutin, 72 of whom received the 40/15 regimen and 28 received the 100/20 regimen. An additional heavily pretreated “special population” of nine patients received 40/12.5 (i.e., a reduced Betalutin dosage) due to low platelets and/or a previous autologous stem cell transplant. Part C was a small expansion cohort of four patients, all receiving the 40/15 regimen, and was designed to obtain supplementary pharmacokinetic data. Results: The efficacy analysis set comprised a total of 100 patients from the PARADIGME study. The overall response rates were 38.9% and 32.1%, and the complete response rates were 20.8% and 14.3% in the 40/15 and 100/20 groups, respectively. Correspondingly, the median response durations were 8.5 months and 3.4 months in the two groups. Hence, increasing the Betalutin activity dose by using the stronger protective CD37 pre-dosing (“100/20”) did not improve the therapeutic benefit. The most common grade ≥ 3 adverse events were hematologic, including neutropenia (11.5%) and thrombocytopenia (8.0%), with nadirs occurring around weeks 5–7 and recovery by week 11. Conclusions: A single-dose administration of Betalutin had a mild toxicity profile with a clinically relevant response rate. It represents a viable treatment alternative in FL patients who are not suitable for more toxic and long-lasting treatments. Trial Registration: This trial was registered at clinicaltrials.gov under #NCT 01796171. Full article

The rapid expansion of the Internet of Things (IoT) has brought forth numerous challenges in testing distributed applications within cloud environments. A significant issue is the latency associated with hosting these applications on cloud computing platforms, despite their potential to improve productivity and reduce costs. This necessitates a reevaluation of existing conformance testing frameworks for cloud environments, with a focus on addressing coordination and observability challenges during data processing. To tackle these challenges, this study proposes a novel approach based on Deep Q-Networks (DQN) and federated learning (FL). In this model, fog nodes train their local models independently and transmit only parameter updates to a central server, where these updates are aggregated into a global model. The DQN agents replace explicit coordination messages with learned decision functions, dynamically determining when and how testers should coordinate. This approach not only preserves the privacy of IoT devices but also enhances the efficiency of the testing process. We provide a comprehensive mathematical formulation of our approach, along with a detailed case study of a Smart City Traffic Management System. Our experimental results demonstrate significant improvements over traditional testing approaches, including a ~58% reduction in coordination messages. These findings confirm the effectiveness of our approach for distributed testing in dynamic environments with varying network conditions. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

Background: Follicular lymphoma (FL) is the second most common B-cell lymphoma in Western countries, typically presenting as an indolent disease with prolonged overall survival. Despite favorable initial responses to therapy, most patients experience relapse, and early progression is associated with poor outcomes. Methods: This guideline provides evidence-based recommendations from the Spanish GELTAMO group on the diagnosis, staging, treatment, and follow-up of FL. A systematic literature review was conducted, and recommendations were graded according to the GRADE system. Results: Histopathological diagnosis should be based on excisional biopsy. PET-CT is recommended for staging and response evaluation. For localized disease, involved-site radiotherapy (ISRT) remains the treatment of choice. In asymptomatic patients with advanced-stage disease and low tumor burden, a watch-and-wait approach is appropriate, although rituximab monotherapy is also acceptable. For advanced-stage disease with high tumor burden, immunochemotherapy with anti-CD20 antibodies (rituximab or obinutuzumab) combined with CHOP, CVP, or bendamustine is recommended, followed by maintenance therapy. Management of relapsed disease is tailored based on tumor burden, treatment history, and timing of relapse. Although novel immunotherapies (CAR-T therapy and bispecific antibodies) are emerging as promising options, autologous stem cell therapies may still be a valid option in young patients with early relapse who are sensitive to immunochemotherapy. Conclusions: FL is a heterogeneous disease requiring individualized management strategies. Recent advances in immunotherapy and molecular diagnostics are reshaping the therapeutic landscape. These updated GELTAMO recommendations aim to provide practical guidance for optimal FL management in clinical practice. Full article

Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations. Full article

Plant growth-promoting (PGP) bacteria are beneficial microbes that can help plants mitigate various biotic and abiotic stresses through different PGP functions. Flavins (FLs) are involved in flavoprotein-mediated reactions essential for plant metabolism and could act as PGP molecules. The aim of this study was to isolate and characterize potential FLs secreting bacteria from apple (Malus domestica [Suckow] Borkh) roots based on their fluorescence and to evaluate their PGP properties, including FLs secretion. A total of 26 bacteria with increased fluorescence in liquid culture were isolated from the apple roots. Based on 16S rRNA sequencing analysis, 11 genetically different strains mostly from Burkholderia and Rhizobia spp. were identified. All isolates secreted considerable amounts of riboflavin. In vitro plant assays showed that under nitrogen (N) limitation, inoculated alfalfa (Medicago sativa) plants yielded at least 25% more dry mass than non-inoculated plants, and inoculation with AK7 and FL112 enriched plant tissue N content compared to non-inoculated plants. This improved N acquisition was not linked to symbiotic N fixation. Additionally, the isolates exhibited some other PGP properties. However, no specific PGP functions were linked to improved plant N acquisition but could potentially be linked to the FLs secretion. For future investigation, the mechanisms underlying improved plant N uptake should be assessed to gain a more in-depth understanding. Full article

Modern engineering systems require reliable and timely Fault Detection and Diagnosis (FDD) to ensure operational safety and resilience. Traditional model-based and rule-based approaches, although interpretable, exhibit limited scalability and adaptability in complex, data-intensive environments. This survey provides a systematic overview of recent studies exploring Machine Learning (ML) and Artificial Intelligence (AI) techniques for FDD across industrial, energy, Cyber-Physical Systems (CPS)/Internet of Things (IoT), and cybersecurity domains. Deep architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks (GNNs) are compared with unsupervised, hybrid, and physics-informed frameworks, emphasizing their respective strengths in adaptability, robustness, and interpretability. Quantitative synthesis and radar-based assessments suggest that AI-driven FDD approaches offer increased adaptability, scalability, and early fault detection capabilities compared to classical methods, while also introducing new challenges related to interpretability, robustness, and deployment. Emerging research directions include the development of foundation and multimodal models, federated learning (FL), and privacy-preserving learning, as well as physics-guided trustworthy AI. These trends indicate a paradigm shift toward self-adaptive, interpretable, and collaborative FDD systems capable of sustaining reliability, transparency, and autonomy across critical infrastructures. Full article

Background: Macrocytosis commonly develops during imatinib therapy, but its relationship with cytogenetic and molecular outcomes in chronic myeloid leukemia (CML) remains unclear. We investigated whether increases in mean corpuscular volume (MCV) during imatinib treatment are associated with response depth and treatment persistence. Methods: In this retrospective study, we analyzed 101 adults with chronic-phase CML treated with a stable imatinib dose of 400 mg/day for at least 12 months. Patients with conditions that could confound MCV (hydroxyurea exposure, megaloblastic anemia, hypothyroidism, chronic liver disease, alcoholism) were excluded. Complete cytogenetic response (CCyR) and major molecular response (MMR) were assessed by conventional karyotyping and the BCR-ABL1 International Scale, respectively. Increased MCV was defined as MCV > 100 fL after six months of therapy, persisting thereafter. Associations between MCV dynamics, response, and switching to second-generation tyrosine kinase inhibitors were evaluated. Results: Twenty patients (20%) developed increased MCV. Overall, 86 patients (85%) achieved CCyR and 70 (69%) achieved MMR. All patients with increased MCV attained CCyR, compared with 66 of 81 (81%) without MCV elevation (p = 0.037), while MMR rates were 90% versus 64% (p = 0.030). During a median follow-up of 69 months, treatment modification was required in 1 of 20 (5%) patients with increased MCV versus 25 of 81 (31%) in the non-increased group (p = 0.018). Conclusions: MCV elevation during imatinib therapy is associated with deeper molecular response and reduced need for treatment modification. MCV dynamics may serve as an inexpensive pharmacodynamic marker to support risk assessment and guide monitoring in chronic-phase CML. Full article

Background/Objectives: Relapsed or refractory follicular lymphoma (rrFL) remains difficult to treat in elderly or frail patients who cannot tolerate standard-dose immuno-chemotherapy as well as novel therapies. Metronomic chemotherapy (mCHEMO) may offer sustained antitumor activity with reduced toxicity. This study assessed the clinical activity and safety of R-DEVEC or R-DEVEC-light in rrFL patients following lenalidomide discontinuation or ineligibility. Methods: Data from the ReLLi Lymphoma Registry (2013–2025) were retrospectively analyzed. Eligible patients had rrFL after ≥1 prior therapy and initiated mCHEMO at least six months before data cutoff. Thirteen patients received DEVEC or the etoposide-free DEVEC-light regimen; all but one also received rituximab. Responders received maintenance vinorelbine, low-dose prednisone, and rituximab, followed by vinorelbine-only maintenance until progression or intolerance. Responses were assessed by CT after cycle two and PET/CT at completion of six induction cycles. Results: median age was 77 years (range 58–92); most patients were frail and had advanced disease. At the end of induction, 84% achieved remission (46% CR, 38% PR), with three PR converting to CR during maintenance. After a median follow-up of 27 months, the PFS was 42% (95CI 15–69%) and the OS 73% (95CI 47–100%). A transformation occurred in one patient; the main toxicity was grade 3 neutropenia (31%). DEVEC-light showed improved tolerability versus full DEVEC, with manageable infections and rare discontinuations. Conclusions: Metronomic R-DEVEC-light is a feasible and effective disease-controlling strategy for frail, heavily pretreated rrFL patients who do not tolerate lenalidomide and are excluded from modern therapies. This schedule warrants further prospective evaluation and exploration in combination with targeted agents. Full article

Automatic sleep-stage classification is a key component of long-term sleep monitoring and digital health applications. Although deep learning models trained on centralized datasets have achieved strong performance, their deployment in real-world healthcare settings is constrained by privacy, data-governance, and regulatory requirements. Federated learning (FL) addresses these issues by enabling decentralized training in which raw data remain local and only model parameters are exchanged; however, its effectiveness under realistic physiological heterogeneity remains insufficiently understood. In this work, we investigate a subject-level federated deep learning framework for sleep-stage classification using polysomnography data from the ISRUC-Sleep dataset. We adopt a realistic one subject = one client setting spanning three clinically distinct subgroups and evaluate a lightweight one-dimensional convolutional neural network (1D-CNN) under four training regimes: a centralized baseline and three federated strategies (FedAvg, FedProx, and FedBN), all sharing identical architecture and preprocessing. The centralized model, trained on a cohort with regular sleep architecture, achieves stable performance (accuracy 69.65%, macro-F1 0.6537). In contrast, naive FedAvg fails to converge under subject-level non-IID data (accuracy 14.21%, macro-F1 0.0601), with minority stages such as N1 and REM largely lost. FedProx yields only marginal improvement, while FedBN—by preserving client-specific batch-normalization statistics—achieves the best federated performance (accuracy 26.04%, macro-F1 0.1732) and greater stability across clients. These findings indicate that the main limitation of FL for sleep staging lies in physiological heterogeneity rather than model capacity, highlighting the need for heterogeneity-aware strategies in privacy-preserving sleep analytics. Full article

Semantic Communication (SC), driven by a deep learning (DL)-based “understand-before-transmit” paradigm, transmits lightweight semantic information (SI) instead of raw data. This approach significantly reduces data volume and communication overhead while maintaining performance, making it particularly suitable for UAV communications where the platform is constrained by size, weight, and power (SWAP) limitations. To alleviate the computational burden of semantic extraction (SE) on the UAV, this paper introduces federated learning (FL) as a distributed training framework. By establishing a collaborative architecture with edge users, computationally intensive tasks are offloaded to the edge devices, while the UAV serves as a central coordinator. We first demonstrate the feasibility of integrating FL into SC systems and then propose a novel solution based on Proximal Policy Optimization (PPO) to address the critical challenge of ensuring service fairness in UAV-assisted semantic communications. Specifically, we formulate a joint optimization problem that simultaneously designs the UAV’s flight trajectory and bandwidth allocation strategy. Experimental results validate that our FL-based training framework significantly reduces computational resource consumption, while the PPO-based algorithm approach effectively minimizes both energy consumption and task completion time while ensuring equitable quality-of-service (QoS) across all edge users. Full article